This work investigates a particular class of artefacts, or ghost sources, in radio interferometric images. Earlier observations with (and simulations of) the Westerbork Synthesis Radio Telescope (WSRT) suggested that these were due to calibration with incomplete sky models. A theoretical framework is derived that validates this suggestion, and provides predictions of ghost formation in a two-source scenario. The predictions are found to accurately match the result of simulations, and qualitatively reproduce the ghosts previously seen in observational data. The theory also provides explanations for many previously puzzling features of these artefacts (regular geometry, PSF-like sidelobes, seeming independence on model flux), and shows that the observed phenomenon of flux suppression affecting unmodelled sources is due to the same mechanism. We demonstrate that this ghost formation mechanism is a fundamental feature of calibration, and exhibits a particularly strong and localized signature due to array redundancy. To some extent this mechanism will affect all observations (including those with non-redundant arrays), though in most cases the ghosts remain hidden below the noise or masked by other instrumental artefacts. The implications of such errors on future deep observations are discussed.

Abstract-Human settlement expansion is one of the most pervasive forms of land cover change in South Africa. The use of Page's Cumulative Sum Test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500 m MODIS time series satellite data. The method is a sequential per pixel change alarm algorithm that can take into account positive detection delay, probability of detection and false alarm probability to construct a threshold. Simulated change data was generated to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before mentioned study areas.

Abstract-It is proposed that the time series extracted from Moderate Resolution Imaging Spectroradiometer satellite data be modeled as a simple harmonic oscillator with additive colored noise. The colored noise is modeled with an Ornstein-Uhlenbeck process. The Fourier transform and maximum likelihood parameter estimation are used to estimate the harmonic and noise parameters of the Colored Simple Harmonic Oscillator. Two case studies in South Africa show that reliable class differentiation can be obtained between natural vegetation and settlement land cover types, when using the parameters of the Colored Simple Harmonic Oscillator as input features to a classifier. The two case studies were conducted in the Gauteng and Limpopo provinces of South Africa. In the case of the Gauteng case study we obtained an average κ = 0.86 for single band classification, while standard harmonic features only achieved an average κ = 0.61. In conclusion the results obtained from the Colored Simple Harmonic Oscillator approach outperformed standard harmonic features and the minimum distance classifier.

We consider the fraction of nodes that default in large, stochastic, inhomogeneous financial networks following an initial shock to the system. Results for deterministic sequences of networks are generalized to stochastic networks to account for interbank lending relationships that change frequently. A general class of inhomogeneous stochastic networks is proposed for use in systemic risk research, and we illustrate how results that hold for Erdős–Rényi networks can be generalized to the proposed network class. The network structure of a system is determined by interbank lending behavior which may vary according to the relative sizes of the banks. We then use the results of the paper to illustrate how network structure influences the systemic risk inherent in large banking systems.

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It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.

Abstract-In this paper, a first order MODIS time series simulator, which uses a Colored Simple Harmonic Oscillator, is proposed. The simulated data can be used to augment data sets so that data intensive classification and change detection algorithms can be applied without enlarging the available ground truth data sets. The simulator's validity is tested by simulating data sets of natural vegetation and human settlement areas and comparing it to the ground truth data in the Gauteng province located in South Africa. The difference found between the real and simulated data sets, which is reported in the experiments is negligent. The simulated and real world data sets are compared by using a wide selection of class and pixel metrics. In particular the average temporal Hellinger distance between the real and simulated data sets is 0.2364 and 0.2269 for the vegetation and settlement class respectively, while the average parameter Hellinger distance is 0.1835 and 0.2554 respectively.